Conversational applications are a type of software that automates dialogue via chat or text-to-speech. Many people engage with conversational AI daily, sometimes without even realising it. This technology already simplifies several tasks in the office. For example, spellcheck and autocomplete.

Conversational apps are widespread; customer service, healthcare, retail, news media, and banking, among others, have successfully implemented chatbots and voice assistants. Popular applications like Apple’s Siri and Google Home also combine IoT devices with speech recognition and machine learning. Amazon’s Alexa has over 30,000 skills to shop, listen to music, and catch up with the news. Google Assistant, available for all Android phones, can answer questions and learn from users to offer personalised information.

Digital healthcare solutions can especially benefit from AI interfaces – from checking how patients are feeling to providing diet tips and managing prescriptions. Chatbots are available 24/7, they can speak hundreds of languages, and integrate with existing legacy solutions. When working alongside doctors and nurses, they can handle information seeking and repetitive interactions, freeing human resources for more complex queries. 

Even more, conversational AI can help companies become more accessible by reducing entry barriers for people that use assistive technologies (such as groups of users of text-to-speech dictation and language translation). 

 

How does conversational AI work?

Conversational applications imitate human-like exchanges between people and computers using chatbots, digital assistants, and virtual agents. To do so, the AI must first understand what a user is trying to communicate – regardless of grammatical mistakes and oddities. Then, it needs to determine the correct response and learn from the exchange. AI conversational apps achieve this interaction by combining large volumes of data with machine learning and NLP (Natural Language Processing). 

Most conversational apps have extensive analytics built into them, which ensure human-like conversational experiences. Healthcare chatbots have been deemed more helpful when they match a human’s integrity and benevolence. They can do so by providing around-the-clock aid to patients while showing empathy, warmth, and friendliness. 

Thanks to machine learning, AI platforms can better recognise patterns and use them to make predictions. With the aid of NLP and NLU (Natural Language Understanding), an app can receive a text or voice input, decipher its meaning, formulate a response, and use algorithms to refine the responses over time. Once the AI determines the correct answer based on its understanding of a user’s intent, it generates a response. This response has to be in a format the user can understand.

Unlike chatbots that follow a predefined workflow, AI-driven conversational software needs to understand users’ requirements and provide reactive and proactive engagement. In a regular conversation, humans remember what they have said from one response to the next. A conversational AI chatbot also needs to remember context from various statements, which goes well beyond recognising keywords or phrases and generating answers based on a script. 

 

Use cases for conversational AI

Most AI chatbots today are focused on problem-solving and improving costs by supporting repetitive custom interactions. Healthcare providers and medical assistants leverage these conversational AI tools to cut unnecessary costs and simplify patient care. On a day-to-day basis, a medical chatbot can register and analyse a patient’s symptoms, advising when to visit an ER and sending their history to a doctor. It can also provide help in emergencies and with first aid.

In online customer support, online chatbots frequently answer FAQs, suggest products, and motivate social media engagement. A healthcare AI chatbot can record notes, schedule medical appointments, generate reports, and order medicines from pharmacies. Many human resources processes can also be aided by conversational AI – for example, by supporting onboarding processes, employee training, and keeping employees’ information up to date. 

There are three prominent use cases for medical chatbots. The first one is informative. This means providing automated information and support through notifications, breaking stories, and health articles. The second is being conversational. This can range from giving pre-built responses to crafting more personal responses aided by NLP and NLU. And the third is being prescriptive or capable of offering therapeutic solutions. For example, people who suffer from anxiety disorders or depression can converse with a chatbot trained in cognitive behavioural therapy (CBT). 

As conversational AI evolves and improves, we can expect greater personalisation and integration throughout the entire healthcare journey.

 

Future of conversational AI in healthcare

We see a constant and steady growth in computing capabilities. Thanks to machine learning and NLP, our AI tools can almost converse like humans. Not only that, but they learn and improve their exchanges as they do so.

In the future, conversational AI could become the first contact point for primary care. A simple yet impactful application is the ability to book doctor appointments. This would include the ability to find a convenient time slot and gathering information useful to the physician. In a hospital environment, a doctor could also use conversational AI to review a patient’s evolution, assign nurses, set reminders and push notifications. 

There are, however, some things to consider when developing a conversational AI for healthcare. The first is the knowledgeability. The chatbot should have access to a database that is thorough and up to date. This could include information ranging from location and operating hours to medical procedures, health screening, and symptoms. The second is engagement. Different chatbots have different ‘personalities’ depending on their use and the brand they represent. Also, a healthcare chatbot needs to be empathetic; its answers should not be merely factual but mimic a human conversation. 

Finally, integration is key to implementing a successful conversational AI in healthcare. The tool must work well with other internal systems to provide users with more personalised answers, for example, by having access to electronic medical records, patient profiles, allergies, and previous treatments. Of course, this data will also need to be stored safely and guarantee patient privacy.

The teaming up of healthcare providers and conversational AI can significantly impact both healthcare workers and human lives. Shortly, we will probably see an increased focus on preventive care. Thanks to conversational AI, chatbots could help provide emotional and mental health support. Hospitals, too, can care for patients in their own homes, freeing up teams to deal with critical cases and emergencies. 

 

Establish remote patient monitoring with FST

Fluffy Spider Technologies is at the forefront of the digitisation of health. We create commercially viable software solutions to integrate conversational AI with healthcare systems.

High quality commercial software requires a dedicated team that has the relevant experience. We can work with you through the entire process, from concept to commercialisation. Get in touch to learn more about how we can help you establish remote patient monitoring practices with our Integration Services.

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